Back out "[nomnigraph][executor] computeChains with nomnigraph" (#15451)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15451

Original commit changeset: ccd050bfead6

Reviewed By: ilia-cher

Differential Revision: D13533161

fbshipit-source-id: 1d0dcd54c2e3875aab015f3e996693e67a449b87

diff --git a/caffe2/core/net_async_base.cc b/caffe2/core/net_async_base.cc
index 8bfecab..6c50dbb 100644 (file)
--- a/caffe2/core/net_async_base.cc
+++ b/caffe2/core/net_async_base.cc
@@ -74,7 +74,7 @@ AsyncNetBase::AsyncNetBase(
   if (FLAGS_caffe2_net_async_inference_mode) {
     execution_chains_ = dag_utils::computeGroups(operator_nodes_);
   } else {
-    execution_chains_ = dag_utils::computeChains(*net_def, operator_nodes_);
+    execution_chains_ = dag_utils::computeChains(operator_nodes_);
   }
   chains_.reserve(execution_chains_.size());
   for (const auto& kv : execution_chains_) {

diff --git a/caffe2/core/net_dag_utils.cc b/caffe2/core/net_dag_utils.cc
index c46b5c5..8786ac6 100644 (file)
--- a/caffe2/core/net_dag_utils.cc
+++ b/caffe2/core/net_dag_utils.cc
@@ -8,12 +8,9 @@
 #include "caffe2/core/operator.h"
 #include "caffe2/core/static_tracepoint.h"
 #include "caffe2/core/timer.h"
-#include "caffe2/opt/converter.h"
 #include "caffe2/proto/caffe2_pb.h"
 #include "caffe2/utils/proto_utils.h"
 
-#include "nomnigraph/Graph/Algorithms.h"
-
 namespace caffe2 {
 namespace dag_utils {
 
@@ -123,146 +120,6 @@ void updateOperatorNodes(
 }
 } // namespace
 
-using namespace nom::repr;
-using DepGraph = nom::Graph<NNGraph::NodeRef>;
-
-// \brief This function prunes edges in the dependency
-// graph to increase the chaining opportunity.
-// It does not eliminate parallelism opportunity.
-void optimizeDependencyGraph(DepGraph* deps) {
-  auto edges = deps->getMutableEdges();
-  for (const auto& edge : edges) {
-    auto tail = edge->tail();
-    auto head = edge->head();
-    deps->deleteEdge(edge);
-    std::unordered_set<DepGraph::NodeRef> seen;
-    nom::algorithm::reachable<DepGraph>(tail, nullptr, &seen);
-    // Removing that edge removes a dominator, which is invalid
-    if (!seen.count(head)) {
-      deps->createEdge(tail, head);
-    }
-  }
-}
-
-ExecutionChains computeChains(
-    const caffe2::NetDef& predict_net,
-    std::vector<OperatorNode>& orig_nodes) {
-  // These serve as the map into predict_net.op()
-  std::vector<NNGraph::NodeRef> nom_ops;
-  auto nn = convertToNNModule(predict_net, false, &nom_ops);
-  CAFFE_ENFORCE_EQ(nom_ops.size(), predict_net.op().size());
-
-  // Create a map from NodeRef to index into predict_net.op()
-  // Now we can use pure nomnigraph functions and map back later
-  std::unordered_map<NNGraph::NodeRef, int> nom_op_to_pos;
-  for (auto idx = 0; idx < nom_ops.size(); ++idx) {
-    nom_op_to_pos[nom_ops[idx]] = idx;
-  }
-
-  // The algorithm:
-  // 1) create dependency graph of ops
-  // 2) for all nodes thats have multiple in edges, remove all in edges
-  // 3) for all nodes thats have multiple out edges, remove all out edges
-  // 4) return the components as chains
-
-  // Caveats that can easily be handled
-  // 1) Cannot have a chain that crosses device options
-  //    insert extra edge at each boundary
-  // 2) All CPU async ops have to be the last op in a chain
-  //    insert extra out edge
-  DepGraph deps;
-
-  // Map NodeRef to the node in the dependency graph
-  std::unordered_map<NNGraph::NodeRef, DepGraph::NodeRef> dep_map;
-  for (const auto& node : nn::filter<NeuralNetOperator>(nn)) {
-    dep_map[node] = deps.createNode(node);
-  }
-
-  // 1) Create dependency graph
-  for (const auto& node : nn::filter<NeuralNetOperator>(nn)) {
-    for (const auto& output : nn::getOutputs(node)) {
-      for (const auto& consumer : nn::getConsumers(output)) {
-        // Record single dependencies first
-        if (!deps.hasEdge(dep_map[node], dep_map[consumer])) {
-          deps.createEdge(dep_map[node], dep_map[consumer]);
-        }
-      }
-    }
-  }
-
-  optimizeDependencyGraph(&deps);
-
-  // Fixup device boundary and async op issues
-  for (const auto& dep : deps.getMutableNodes()) {
-    int op_idx = nom_op_to_pos[dep->data()];
-    auto d1 = orig_nodes.at(op_idx).operator_->device_option();
-    auto outEdges = dep->getOutEdges();
-    for (const auto& outEdge : outEdges) {
-      int op2_idx = nom_op_to_pos[outEdge->head()->data()];
-      auto d2 = orig_nodes.at(op2_idx).operator_->device_option();
-      if (!IsSameDevice(d1, d2)) {
-        deps.createEdge(dep, outEdge->head());
-      }
-    }
-    if (d1.device_type() == PROTO_CUDA) {
-      continue;
-    }
-    if (orig_nodes.at(op_idx).operator_->HasAsyncPart()) {
-      outEdges = dep->getOutEdges();
-      for (const auto& outEdge : outEdges) {
-        // Clone out edges
-        deps.createEdge(outEdge->tail(), outEdge->head());
-      }
-    }
-  }
-
-  // 2) Prune in edges if multiplicity > 1
-  // 3) Prune out edges if multiplicity > 1
-  for (const auto& dep : deps.getMutableNodes()) {
-    auto inEdges = dep->getInEdges();
-    if (inEdges.size() > 1) {
-      for (const auto& inEdge : inEdges) {
-        NOM_REQUIRE_OR_CONT(inEdge);
-        deps.deleteEdge(inEdge);
-      }
-    }
-    auto outEdges = dep->getOutEdges();
-    if (outEdges.size() > 1) {
-      for (const auto& outEdge : outEdges) {
-        NOM_REQUIRE_OR_CONT(outEdge);
-        deps.deleteEdge(outEdge);
-      }
-    }
-  }
-
-  // 4) Return components as chains
-  std::vector<DepGraph::NodeRef> chain_starts;
-  for (const auto& dep : deps.getMutableNodes()) {
-    if (dep->getInEdges().size() == 0) {
-      chain_starts.emplace_back(dep);
-    }
-  }
-
-  ExecutionChains chains;
-  for (const auto& dep : chain_starts) {
-    DepGraph::NodeRef front = dep;
-    std::vector<int> ops;
-    do {
-      ops.emplace_back(nom_op_to_pos[front->data()]);
-      auto outEdges = front->getOutEdges();
-      if (outEdges.size()) {
-        front = outEdges.at(0)->head();
-      } else {
-        front = nullptr;
-      }
-    } while (front);
-    chains[nom_op_to_pos[dep->data()]] = ops;
-  }
-
-  updateOperatorNodes(orig_nodes, chains);
-  return chains;
-}
-
 ExecutionChains computeChains(std::vector<OperatorNode>& orig_nodes) {
   const std::vector<OpGraphNode> nodes = pruneOpNodeGraph(orig_nodes);
   vector<int> initial_frontier;

diff --git a/caffe2/core/net_dag_utils.h b/caffe2/core/net_dag_utils.h
index 930f8e4..9b605a9 100644 (file)
--- a/caffe2/core/net_dag_utils.h
+++ b/caffe2/core/net_dag_utils.h
@@ -43,9 +43,6 @@ struct OpGraphNode {
 
 using ExecutionChains = std::unordered_map<int, std::vector<int>>;
 
-C10_EXPORT ExecutionChains computeChains(
-    const caffe2::NetDef& predict_net,
-    std::vector<OperatorNode>& orig_nodes);
 C10_EXPORT ExecutionChains computeChains(std::vector<OperatorNode>& orig_nodes);
 
 // Instead of breaking down the DAG into chains, we partition it into clusters

diff --git a/caffe2/python/test/executor_test.py b/caffe2/python/test/executor_test.py
index 8c9a42b..ee52717 100644 (file)
--- a/caffe2/python/test/executor_test.py
+++ b/caffe2/python/test/executor_test.py
@@ -2,16 +2,14 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from caffe2.python import core, workspace, model_helper
-import random
+from caffe2.python import core, workspace
 from caffe2.python.test.executor_test_util import (
     build_conv_model,
     build_resnet50_dataparallel_model,
     run_resnet50_epoch,
     ExecutorTestBase,
     executor_test_settings,
-    executor_test_model_names,
-)
+    executor_test_model_names)
 
 from caffe2.python.test_util import TestCase
 
@@ -26,12 +24,10 @@ ITERATIONS = 1
 
 
 class ExecutorCPUConvNetTest(ExecutorTestBase):
-    @given(
-        executor=st.sampled_from(EXECUTORS),
-        model_name=st.sampled_from(executor_test_model_names()),
-        batch_size=st.sampled_from([1]),
-        num_workers=st.sampled_from([8]),
-    )
+    @given(executor=st.sampled_from(EXECUTORS),
+           model_name=st.sampled_from(executor_test_model_names()),
+           batch_size=st.sampled_from([1]),
+           num_workers=st.sampled_from([8]))
     @executor_test_settings
     def test_executor(self, executor, model_name, batch_size, num_workers):
         model = build_conv_model(model_name, batch_size)
@@ -54,7 +50,8 @@ class ExecutorCPUConvNetTest(ExecutorTestBase):
 @unittest.skipIf(not workspace.has_gpu_support
                 and not workspace.has_hip_support, "no gpu")
 class ExecutorGPUResNetTest(ExecutorTestBase):
-    @given(executor=st.sampled_from(EXECUTORS), num_workers=st.sampled_from([8]))
+    @given(executor=st.sampled_from(EXECUTORS),
+           num_workers=st.sampled_from([8]))
     @executor_test_settings
     def test_executor(self, executor, num_workers):
         model = build_resnet50_dataparallel_model(
@@ -103,33 +100,5 @@ class ExecutorFailingOpTest(TestCase):
         self.assertFalse(res)
 
 
-class ExecutorFuzzTest(ExecutorTestBase):
-    def test_fuzzy_model(self):
-        model = model_helper.ModelHelper(name="test")
-        inits = []
-        for i in range(100):
-            init = model.param_init_net.ConstantFill(
-                [], "ONE" + str(i), shape=[1], value=1.0
-            )
-            inits.append(init)
-        adds = []
-        for i in range(1000):
-            add = model.net.Add(
-                [random.choice(inits + adds), random.choice(inits + adds)],
-                "ADD" + str(i),
-            )
-            adds.append(add)
-
-        def run_model():
-            workspace.RunNet(model.net, 100)
-
-        self.compare_executors(
-            model,
-            ref_executor="simple",
-            test_executor="async_scheduling",
-            model_run_func=run_model,
-        )
-
-
-if __name__ == "__main__":
+if __name__ == '__main__':
     unittest.main()